This vignette demonstrates how to reassign residual (filtered out) transcripts to the neighboring cells. For this, we will use the results of the vignette on running SPLIT on Xenium
if(!requireNamespace("spacexr", quietly = TRUE)){
remotes::install_github("dmcable/spacexr") ## or remotes::install_github("jpromeror/spacexr@HD") for implementation of the doublet mode.
}
library(spacexr)
if(!requireNamespace("SPLIT", quietly = TRUE)){
remotes::install_github("bdsc-tds/SPLIT")
}
library(SPLIT)
library(dplyr)
library(Seurat)
library(SingleCellExperiment)
library(ggplot2)We load the Xenium spatial transcriptomics data using the
STexampleData package, which provides convenient access to
example spatial datasets for analysis and demonstration.
if(!requireNamespace("STexampleData", quietly = TRUE))
remotes::install_github("lmweber/STexampleData")
xe_full_seu <- STexampleData::Janesick_breastCancer_Xenium_rep1() ## see ?STexampleData and browseVignettes('STexampleData') for documentation
## loading from cache## Convert to Seurat to stay consistent with chromium object
sp_coords <- spatialCoords(xe_full_seu)
colnames(sp_coords) <- c("ST_1", "ST_2")
xe <- CreateSeuratObject(
counts = counts(xe_full_seu),
assay = "Xenium",
meta.data = as.data.frame(colData(xe_full_seu))
)
xe[["spatial"]] <- CreateDimReducObject(sp_coords, assay = "Xenium", key = "ST_")
xe$x <- sp_coords[,1]
xe$y <- sp_coords[,2]
rm(xe_full_seu)You can optionally crop the slice to reduce computations, but we will use the entire dataset.
DO_subset_xe <- FALSE
X_lim <- c(6000, Inf) # cropping area
Y_lim <- c(4000, Inf) # cropping area
if(DO_subset_xe){
xe <- subset(xe, subset = x > min(X_lim) & x < max(X_lim) & y > min(Y_lim) & y < max(Y_lim))
} Download pre-computed RCTD (ie., cell-type annotation) results
# Install googledrive if you haven't already
if (!requireNamespace("googledrive", quietly = TRUE)) {
install.packages("googledrive")
}
library(googledrive)
drive_deauth()
# Define the file ID from the Google Drive link
file_id <- "1pTUKq49JbUFwVk7vttjZIFqkx-AKznRF"
local_path <- tempfile(fileext = ".rds")
drive_download(as_id(file_id), path = local_path, overwrite = TRUE)## File downloaded:
## • 'precomp_rctd_class_aware.rds' <id: 1pTUKq49JbUFwVk7vttjZIFqkx-AKznRF>
## Saved locally as:
## • '/tmp/Rtmpt49rM9/file2114f52c2b811.rds'Visualize RCTD Annotation Post-process RCDT output and add results into Xenium object
## Correcting singlets ...
## Updating scores ...
## Add coordinates to results ...
## Computing alternative annotations ...
## Replacing results_df ...xe <- AddMetaData(xe, RCTD@results$results_df)
xe <- subset(xe, subset = nCount_Xenium >= 10)
xe <- xe %>% SCTransform(assay = "Xenium", verbose = FALSE) %>% RunPCA(verbose = FALSE) %>% RunUMAP(dims = 1:50, verbose = FALSE)# and define colors for reproducibility
library(RColorBrewer)
cell_types <- unique(xe$first_type) %>% sort()
colors <- brewer.pal(n = max(3, min(length(cell_types), 12)), name = "Set3")
# Recycle colors if not enough
colors <- rep(colors, length.out = length(cell_types))
pal <- setNames(colors, cell_types)p1 <- UMAPPlot(xe, group.by = "first_type", label = T, repel = T, cols = pal) + theme_void() + theme(aspect.ratio = 1, legend.position = "none")
p2 <- UMAPPlot(xe, group.by = "second_type", cols = pal) + theme_void() + theme(aspect.ratio = 1, legend.position = "bottom")
p3 <- UMAPPlot(xe, group.by = "spot_class") + theme_void() + theme(aspect.ratio = 1, legend.position = "right")
p1 | p2 | p3
Spatial Visualization
This section runs the default SPLIT purification and visualizes purified data.
# Run SPLIT purification
res_split <- SPLIT::purify(
counts = GetAssayData(xe, assay = 'Xenium', layer = 'counts'), # or any gene x cells counts matrix
rctd = RCTD,
DO_purify_singlets = TRUE
)## [1] 307 163849
## N_genes = 307
## Processing certain doublets...
## 158702
## 0 %
## 6 %
## 13 %
## 19 %
## 25 %
## 32 %
## 38 %
## 44 %
## 50 %
## 57 %
## 63 %
## 69 %
## 76 %
## 82 %
## 88 %
## 95 %
## Processing uncertain doublets...
## 2830
## 0 %
## Combaning doublets results ...
## Processed 10000 / 161532
## Processed 20000 / 161532
## Processed 30000 / 161532
## Processed 40000 / 161532
## Processed 50000 / 161532
## Processed 60000 / 161532
## Processed 70000 / 161532
## Processed 80000 / 161532
## Processed 90000 / 161532
## Processed 100000 / 161532
## Processed 110000 / 161532
## Processed 120000 / 161532
## Processed 130000 / 161532
## Processed 140000 / 161532
## Processed 150000 / 161532
## Processed 160000 / 161532
## Processed 161532 / 161532# Create a purified Seurat object
xe_purified <- CreateSeuratObject(
counts = res_split$purified_counts,
meta.data = res_split$cell_meta,
assay = "Xenium"
)Running a standard analysis on purified data to get new (purified) UMAP
# Optional: Filter, normalize and visualize
xe_purified <- subset(xe_purified, subset = nCount_Xenium > 5)
xe_purified <- xe_purified %>%
SCTransform(assay = "Xenium", verbose = FALSE) %>%
RunPCA(verbose = FALSE) %>%
RunUMAP(dims = 1:20, verbose = FALSE)
#UMAPPlot(xe_purified, group.by = c("first_type"), label = T, repel = T) + theme_void() + theme(aspect.ratio = 1, legend.position = "none")p1 <- UMAPPlot(xe, group.by = c("first_type"), label = T, repel = T, cols = pal) + theme_void() + theme(aspect.ratio = 1, legend.position = "none") + ggtitle("Raw Xenium data")
p2 <- UMAPPlot(xe_purified, group.by = c("first_type"), label = T, repel = T, cols = pal) + theme_void() + theme(aspect.ratio = 1, legend.position = "right") + ggtitle("SPLIT-purified Xenium data")
p3 <- UMAPPlot(xe_purified, group.by = c("spot_class")) + theme_void() + theme(aspect.ratio = 1, legend.position = "bottom") + ggtitle("SPLIT-purified Xenium data colored by spot class")
p4 <- UMAPPlot(xe_purified, group.by = c("purification_status")) + theme_void() + theme(aspect.ratio = 1, legend.position = "bottom") + ggtitle("SPLIT-purified Xenium data colored by purification status")
(p1|p2) 
For this, we need to compute: - spatial network to define neighboring
cells to which the residual transcripts will be reassigned - reassign
residual counts (raw_counts - purified_counts) among cell’s
neighboring cells of the secondary cell type (done by
reassign_residual_counts())
sp_nw <- SPLIT::build_spatial_network(
xe,
reduction = "spatial",
dims = 1:2,
DO_prune = TRUE,
rad_pruning = 15, # remove connections further than 15um
k_knn = 20
)## Computing nearest neighbors
## Only one graph name supplied, storing nearest-neighbor graph only
## N = 2424044 ( 78 %) edges were prunedsp_nw <- SPLIT::add_spatial_metric(spatial_neighborhood = sp_nw, rctd = RCTD)
sp_neigh_df <- SPLIT::neighborhood_analysis_to_metadata(sp_nw)
xe <- AddMetaData(xe, sp_neigh_df)
#rm(sp_nw, sp_neigh_df)Compute reassignment
pur_genes <- rownames(xe_purified)
pur_cells <- colnames(xe_purified)
pur_counts <- GetAssayData(xe_purified, layer = "counts", assay = "Xenium") #[pur_genes, pur_cells]
raw_counts <- GetAssayData(xe, layer = "counts", assay = "Xenium") #[pur_genes, pur_cells]
reassignmet_results <- SPLIT::reassign_residual_counts(
raw_counts = raw_counts,
corrected_counts = pur_counts,
spatial_network = sp_nw,
purification_status = xe_purified$purification_status,
mode = "count_proportional", # "uniform" for uniform distribution among neighbors of contaminating cell type, or "count_proportional" for the reassignment proportional to contaminating neighbors' number of counts
return_reassignment_operator = TRUE # optional, to explore which cells effectively send transcripts and which cells receive (see details below)
)
reassigned_counts <- reassignmet_results$corrected_counts
reassignment_operator <- reassignmet_results$reassignment_operator
#rm(reassignmet_results)We can now explore which cells sent transcripts and which cells received them upon re-assignment.
reassignment_operator <- reassignment_operator[pur_cells, pur_cells]
cells_that_uneffectively_send_transccripts <- rownames(reassignment_operator)[rowSums(reassignment_operator) == 0]
receiving <- colSums(reassigned_counts)
sending <- colSums(raw_counts[pur_genes, pur_cells] - pur_counts[pur_genes, pur_cells])
sending_effectively <- sending
sending_effectively[cells_that_uneffectively_send_transccripts] <- 0
sending_receiving <- data.frame(nCount_sent = sending, nCount_sent_effectively = sending_effectively, nCount_received = receiving)
plot(sending_receiving$nCount_sent, sending_receiving$nCount_sent_effectively)
cell.meta <- xe_purified@meta.data
xe_purified_balanced_score <- CreateSeuratObject(
counts = pur_counts,
assay = "Xenium"
)
xe_post_redistribution <- CreateSeuratObject(
counts = reassigned_counts[pur_genes, pur_cells], # no need to add purified counts as they have been already accounted in `reassigned_counts`
meta.data = res_split$cell_meta,
assay = "Xenium"
)
xe_post_redistribution <- AddMetaData(xe_post_redistribution, sending_receiving)
xe_post_redistribution$nCount_Xenium## 1 2 8 9 10 11 14 15 16 17 18 20 23 26 27
## 26.000000 37.840823 43.923451 11.197165 61.380218 39.272797 24.825798 103.621569 50.527331 7.930965 49.965610 12.230983 42.968316 26.132306 13.011576
## 30 32 35 39 40 42 43 44 45 46 47 48 49 53 56
## 48.491993 18.498101 19.586945 47.387264 7.698639 16.301487 55.903330 35.202101 13.435353 15.072056 35.000000 14.052568 23.579705 38.918512 44.418019
## 57 61 65 66 67 68 69 70 71 72 73 74 75 76 77
## 73.592892 36.725213 20.844868 38.482130 17.863005 149.079358 216.744259 105.726632 75.364887 97.509582 87.044351 67.233153 195.000000 45.204063 122.507556
## 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92
## 87.508437 47.808918 60.590797 129.000000 96.434565 39.844596 185.775489 125.596246 138.794653 101.424106 42.775661 119.219207 134.860623 81.666493 146.560519
## 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
## 102.123416 31.162442 135.316753 181.206571 74.063706 92.673041 75.798360 75.781177 127.842279 84.135924 111.000000 81.569780 142.000000 158.653733 360.234542
## 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122
## 97.704432 173.547417 61.254006 132.756377 144.125581 260.000000 172.104053 99.111578 73.540513 140.681654 357.609842 143.438113 141.514391 505.696625 112.192657
## 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137
## 93.588892 376.445264 101.060722 296.692488 98.696523 46.000000 141.305920 257.000000 275.476125 23.135666 276.968012 239.236385 182.527385 135.286643 222.034981
## 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152
## 208.207197 242.550305 246.232341 189.498077 237.979374 146.249962 625.490910 362.373102 340.698393 156.195678 331.972021 52.000000 105.783971 390.000000 204.505165
## 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
## 259.004871 301.515738 241.223506 103.039379 170.000000 276.000000 323.642933 218.137585 227.000000 399.000000 195.133555 112.685730 419.000000 93.000000 424.000000
## 168 169 170 171 172 173 174 175 176 177 178 180 182 183 184
## 92.017814 134.769681 84.628541 263.359431 115.563297 248.723333 187.000000 84.238650 12.549253 195.120394 485.694744 110.824510 111.372944 23.066955 134.000000
## 185 186 188 189 191 192 193 195 196 197 198 199 201 202 203
## 41.389732 212.000000 204.582919 24.909142 14.309316 45.442352 9.942865 178.256397 28.473766 76.267766 258.216992 224.000000 328.156363 262.928854 96.951178
## 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218
## 180.000000 77.000000 182.064767 271.000000 88.727009 150.000000 167.613641 105.919530 90.000000 148.000000 97.846713 103.000000 63.000000 96.776923 109.828626
## 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
## 99.000000 80.000000 118.224876 64.044171 199.291033 238.000000 112.000000 268.000000 79.663337 141.964202 148.000000 214.000000 204.000000 144.890281 61.331023
## 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248
## 354.505414 167.000000 52.294831 333.397957 96.883326 278.000000 265.844554 33.356377 277.000000 102.687521 392.673470 76.495428 192.000215 202.217635 155.028511
## 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263
## 17.257505 167.184214 108.172169 156.000000 44.497309 199.659256 134.656063 214.656092 262.000000 364.344355 313.214260 260.000000 130.794467 160.538998 23.935644
## 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278
## 192.000000 110.540622 135.440392 249.274948 218.767108 284.000000 76.168282 91.036806 53.320371 150.828527 283.757772 241.150618 157.855720 51.299793 165.154641
## 279 280 282 283 284 285 286 287 288 289 290 291 292 293 294
## 221.145005 83.379618 87.897677 97.229868 285.943114 95.843475 296.000000 232.717553 143.468829 177.811640 178.105897 49.672136 515.221967 266.866827 69.219771
## 295 296 297 298 299 300 301 302 303 304 305 306 308 309 310
## 53.986187 160.384190 95.376066 26.962950 63.563800 72.750791 28.913744 93.549062 198.073464 142.929470 23.536288 88.417332 123.000000 165.648785 245.409837
## 311 312 313 314 315 316 317 318 320 321 322 323 324 325 326
## 260.424402 85.331747 238.512832 112.202365 287.391479 96.354546 177.000000 305.137799 51.045445 611.554296 100.060226 127.488362 35.238247 27.532422 83.768602
## 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341
## 226.666203 46.137382 116.000000 130.544458 57.259912 386.458637 233.322797 162.354496 417.000000 153.593313 57.298594 117.506577 187.489155 107.350651 100.861124
## 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356
## 148.463363 183.000000 435.582668 138.413997 89.422297 124.058565 47.306978 514.693022 92.240091 45.063778 51.041218 56.864803 41.044538 118.060853 137.550156
## 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371
## 56.785283 37.978901 84.558552 82.860258 94.057361 77.336771 39.003285 77.188848 65.879959 122.635068 75.221956 58.222803 77.249387 44.912574 41.625185
## 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386
## 79.813001 119.822985 72.822695 209.821147 47.241499 126.887892 94.428526 29.377330 111.419872 156.231208 39.941244 45.185014 85.135693 28.381931 60.631934
## 387 388 389 390 391 392 393 394 395 396 397 399 400 402 403
## 118.394981 45.448051 51.582852 30.352939 46.538911 177.000000 192.912635 79.445505 16.355081 81.471894 93.519312 22.261456 94.561591 38.898442 63.806498
## 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418
## 20.841090 23.012098 80.434096 126.987519 103.000000 45.375125 111.000000 142.000000 214.000000 99.000000 79.000000 376.000000 356.000000 403.000000 209.868933
## 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433
## 186.000000 240.313980 261.513423 358.966822 108.472214 358.772480 488.000000 230.420460 285.155917 269.799877 52.694383 165.617645 227.794677 276.264774 134.547390
## 434 435 436 437 438 439 440 441 443 444 446 447 448 449 450
## 311.675879 245.205784 202.787334 316.455568 210.536236 22.325859 96.693328 24.827038 87.000000 94.463654 115.546154 330.854688 452.638227 197.195659 517.907394
## 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465
## 237.046712 332.861550 207.074372 154.430628 333.927297 100.582497 153.002846 133.154360 142.209439 73.072703 248.171705 366.567563 136.349595 92.019414 115.969636
## 466 467 468 469 470 471 472 473 474 475 477 478 479 480 481
## 129.184980 86.637372 107.435437 106.206374 217.181458 122.446118 222.566011 86.226467 169.312499 255.921865 307.972249 20.011343 182.433370 42.000000 58.433644
## 482 483 484 485 486 488 489 490 491 492 493 494 495 496 497
## 33.000000 74.000000 16.011179 45.103741 29.825705 44.249204 176.825477 149.137413 129.522051 93.640108 191.207391 81.850313 132.438680 60.821728 126.726639
## 498 499 500 501 502 503 504 505 506 507 508 509 511 512 513
## 98.422107 105.094098 73.314272 77.485581 118.000000 97.392924 130.186589 85.258235 131.955296 145.656332 152.804633 82.778769 46.784720 195.359259 59.391070
## 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
## 74.724240 83.871695 153.399417 199.899399 36.479729 108.759400 50.473905 63.118283 125.040039 105.540230 61.576185 110.280912 356.395621 28.445030 106.284104
## 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543
## 197.000000 167.557928 91.364087 43.529483 134.051099 69.483372 60.000000 16.384662 67.227766 48.806222 135.745085 37.000000 21.682092 37.053817 187.252259
## 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558
## 66.884810 119.333092 140.753028 126.179204 139.205271 73.616137 66.033020 97.201506 66.424207 77.000000 75.230211 45.197046 274.106553 117.893447 164.632178
## 559 560 561 562 563 564 565 566 567 568 569 570 571 572 574
## 97.905652 107.687258 118.439408 53.816535 113.767975 13.147827 190.873725 60.912937 162.431045 117.591424 97.373725 110.507453 55.596080 66.388874 436.672052
## 575 576 577 578 579 581 582 583 584 585 586 587 588 589 590
## 26.385679 237.731800 115.319173 59.711885 43.721161 35.708921 199.449535 149.822650 137.153646 33.533333 33.243109 216.445866 22.777126 50.957285 71.506114
## 592 593 594 595 596 597 598 601 603 604 605 606 608 609 610
## 97.620684 31.841298 95.369594 70.294994 35.763399 81.313346 73.095040 121.470894 53.475170 59.895344 15.680013 36.856808 74.088215 78.000000 116.741334
## 611 612 613 614 616 617 618 619 620 621 622 623 624 625 626
## 61.000000 141.000000 119.662736 136.750475 24.842153 94.171769 23.432935 9.824396 102.423812 11.497239 59.878465 121.147249 124.516413 39.000000 80.149895
## 627 628 629 630 631 632 633 634 635 638 641 642 643 644 645
## 85.287852 70.717697 125.814218 157.790627 359.000000 229.685770 45.376245 74.000000 59.621422 100.864142 68.878478 123.963095 201.055098 55.251946 83.281033
## 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660
## 82.000000 154.795451 61.026438 173.264091 73.852350 153.654306 26.288655 31.121881 71.401704 109.671106 38.290379 86.796375 60.645281 111.477216 15.000000
## 661 662 663 664 665 666 667 668 669 670 671 672 673 675 676
## 78.151925 71.268011 57.289255 34.367527 46.573552 40.528468 123.457507 161.467692 50.013498 159.577742 35.000000 43.579539 45.457806 131.205995 193.117264
## 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692
## 201.463361 86.255828 114.478316 170.119547 161.372654 265.153880 99.704631 84.188992 138.927022 31.000000 63.385607 23.545271 90.566291 111.478427 43.725270
## 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707
## 109.620385 249.303196 189.293855 104.595043 157.845446 72.906237 93.800562 155.707513 49.843993 165.895172 160.672438 31.137045 49.049375 194.181835 69.070250
## 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722
## 50.450276 81.093398 69.388516 84.683409 68.051458 101.245671 39.780666 131.584429 107.605403 132.838304 98.283530 52.394925 126.482065 88.000000 129.593295
## 723 724 725 726 727 728 730 731 732 733 734 735 736 737 738
## 96.599907 141.360225 182.567998 159.000000 143.398429 160.236642 68.676272 92.925298 78.340390 161.549881 332.493567 118.775717 134.561337 148.117704 144.180587
## 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753
## 159.378316 148.546101 262.847686 101.234643 46.925821 138.546816 198.897908 92.126703 96.988820 215.032788 97.094275 78.237895 13.468232 55.040732 89.265248
## 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 307.974922 60.959970 254.895000 41.237246 306.655897 114.082521 131.574063 154.014730 182.535992 406.297696 153.447693 60.951114 289.016315 572.000000 300.212702
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783
## 274.000000 215.027223 158.000000 258.836879 267.000000 176.000000 275.475093 183.262254 289.000000 295.966452 82.921412 248.736808 91.000000 160.000000 329.199189
## 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798
## 110.456926 170.053505 119.782997 94.000000 105.287808 147.000000 14.718077 115.924386 187.707907 60.171027 74.521827 74.221809 26.000000 72.000000 50.000000
## 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813
## 68.021313 66.349656 47.240476 123.184574 88.888230 53.921471 78.390217 106.845013 121.713481 81.875936 88.755700 58.851296 99.541237 89.682012 175.753252
## 814 815 816 817 818 819 820 821 822 823 824 825 826 827 829
## 284.515149 127.016681 83.187129 198.998655 13.366547 155.232802 27.201926 164.751930 70.743562 56.528537 278.994502 84.902783 33.095403 110.717928 125.000000
## 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844
## 84.987407 90.419119 171.000000 43.172863 75.768388 260.187617 109.000000 91.179927 162.380295 46.176504 190.570845 107.394720 101.029623 110.735745 128.555261
## 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859
## 120.643482 58.715570 63.240469 210.111770 159.061214 86.766637 108.112853 55.708567 73.184461 65.723509 94.313516 81.749781 86.035168 77.664083 50.622291
## 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874
## 58.182250 285.112779 300.697671 92.001626 238.280490 100.862303 110.921976 173.211651 273.401574 386.173515 91.201617 101.682996 189.292487 64.042412 64.000000
## 875 876 877 879 880 881 882 883 884 885 886 887 888 889 890
## 77.000000 277.840490 213.823496 186.799410 92.000000 27.346620 166.017925 82.325315 218.946055 227.000000 85.755015 109.131953 66.330845 82.689158 101.240552
## 891 892 895 896 897 898 899 901 902 904 905 906 907 908 909
## 82.753854 71.799979 204.770990 238.397044 425.440089 19.547919 120.811268 343.859798 66.150592 87.000000 61.191345 58.954963 51.431092 67.000000 125.517190
## 910 911 912 913 914 915 916 918 919 920 921 922 923 924 925
## 90.959862 64.269321 21.905526 28.173132 67.306827 28.036220 52.111173 102.889705 13.436796 137.793816 130.579798 21.000000 51.087580 72.000000 23.442042
## 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940
## 190.921892 134.768705 91.087024 101.293070 203.050924 171.145622 168.658364 328.015694 270.068524 324.528912 194.836506 271.372681 341.000000 305.000000 344.066468
## 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955
## 245.193763 24.454865 80.001331 149.563824 68.508936 143.748413 121.415654 57.243366 127.032060 126.208659 46.498885 156.000000 240.000000 143.062266 146.000000
## 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970
## 103.679513 54.312588 27.772109 116.000000 46.088343 423.757051 126.391312 152.204748 122.000000 116.461030 79.000000 223.442983 49.000000 155.937563 170.797546
## 971 973 974 975 976 977 978 979 980 981 982 983 984 985 986
## 351.000000 76.804749 138.199471 187.714591 85.049979 195.455095 108.244183 47.265736 147.606450 129.939790 150.000000 186.000000 139.000000 225.000000 130.000000
## 987 988 990 991 992 993 994 995 996 997 998 999 1000 1001 1002
## 72.000000 111.162389 75.078137 99.120114 50.244289 255.733931 286.965909 77.332155 88.009418 162.615089 277.757649 247.714431 183.000000 158.393550 101.619977
## 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1014 1015 1016 1017 1018
## 334.000000 121.265619 153.204855 176.756353 147.033997 271.069630 513.179869 401.817812 99.040557 118.934536 262.586419 87.596575 338.926256 138.899587 162.165294
## 1019 1020 1021 1022 1023 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034
## 110.807957 95.614274 61.464891 55.473425 17.248604 150.713553 150.786030 51.496588 65.552761 27.494671 128.824613 67.279659 177.936869 27.201021 93.374587
## 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049
## 23.175051 65.422964 66.711500 99.788301 104.911451 58.019582 79.448644 36.049210 101.261757 33.634181 46.081043 58.168310 41.739614 35.608983 11.285010
## 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1064 1065 1067
## 28.999981 18.424519 43.790397 25.633732 24.185646 38.715349 35.664776 71.783370 21.824296 35.275818 13.406364 35.773395 16.528016 55.898390 26.792074
## 1068 1070 1072 1073 1074 1076 1079 1080 1081 1082
## 23.013005 14.084435 23.607497 67.793197 53.978986 32.487972 84.239556 52.839983 51.970844 86.780737
## [ reached 'max' / getOption("max.print") -- omitted 160420 entries ]## 1 2 8 9 10 11 14 15 16 17 18 20 23 26 27
## 26.000000 37.840823 38.000000 11.197165 61.380218 27.893810 24.825798 89.552534 33.458851 7.930965 23.654019 12.230983 22.473773 16.713080 13.011576
## 30 32 35 39 40 42 43 44 45 46 47 48 49 53 56
## 48.491993 18.498101 6.888432 47.387264 7.698639 16.301487 55.903330 13.603389 8.391121 13.334448 35.000000 14.052568 17.688409 12.149495 9.010911
## 57 61 65 66 67 68 69 70 71 72 73 74 75 76 77
## 73.592892 26.107001 20.844868 38.482130 17.863005 149.079358 216.744259 89.000000 45.499107 97.509582 87.044351 58.277504 195.000000 45.204063 116.177376
## 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92
## 81.499353 47.808918 57.563319 129.000000 91.893349 39.844596 185.775489 120.000000 132.372731 95.000000 42.775661 53.000000 117.327900 69.430117 146.560519
## 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107
## 92.204904 22.683693 120.589265 181.206571 74.063706 92.673041 75.798360 75.781177 127.842279 84.135924 111.000000 81.569780 142.000000 135.111812 360.234542
## 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122
## 97.704432 173.547417 61.254006 132.756377 144.125581 260.000000 172.104053 99.111578 73.540513 122.000000 58.961021 143.438113 141.514391 228.296780 112.192657
## 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137
## 93.588892 376.445264 101.060722 296.692488 98.696523 46.000000 141.305920 257.000000 275.476125 23.135666 276.968012 239.236385 178.350792 76.644213 174.625136
## 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152
## 203.459277 197.040963 208.208393 160.750811 183.205337 146.249962 555.000000 362.373102 340.698393 156.195678 331.972021 52.000000 105.783971 390.000000 204.505165
## 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
## 259.004871 301.515738 241.223506 103.039379 170.000000 276.000000 323.642933 198.000000 227.000000 399.000000 195.133555 99.000000 419.000000 93.000000 424.000000
## 168 169 170 171 172 173 174 175 176 177 178 180 182 183 184
## 78.332085 134.769681 84.628541 236.000000 115.563297 230.268538 187.000000 84.238650 12.549253 195.120394 427.000000 91.000000 111.372944 17.870993 134.000000
## 185 186 188 189 191 192 193 195 196 197 198 199 201 202 203
## 41.389732 212.000000 170.704767 24.909142 12.000000 43.847151 9.942865 178.256397 28.473766 76.267766 206.959596 224.000000 225.274495 231.000000 87.708023
## 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218
## 180.000000 77.000000 182.064767 271.000000 88.727009 150.000000 167.613641 105.919530 90.000000 148.000000 97.846713 103.000000 63.000000 96.776923 109.828626
## 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233
## 99.000000 80.000000 118.224876 64.044171 176.472587 238.000000 112.000000 268.000000 79.013014 141.964202 148.000000 214.000000 204.000000 144.890281 61.331023
## 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248
## 354.505414 167.000000 52.294831 299.000000 96.883326 278.000000 212.532757 33.356377 277.000000 102.687521 331.092188 53.968015 151.532515 202.217635 133.572360
## 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263
## 17.257505 167.184214 46.356383 156.000000 44.497309 170.003164 134.656063 185.000000 262.000000 364.344355 313.214260 260.000000 130.794467 110.749589 23.935644
## 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278
## 192.000000 110.540622 135.440392 177.000000 218.767108 284.000000 76.168282 91.036806 53.320371 150.828527 283.757772 241.150618 157.855720 51.299793 165.154641
## 279 280 282 283 284 285 286 287 288 289 290 291 292 293 294
## 191.221198 83.379618 66.127544 97.229868 70.000000 95.843475 296.000000 232.717553 143.468829 177.811640 154.000000 49.672136 444.000000 196.909984 69.219771
## 295 296 297 298 299 300 301 302 303 304 305 306 308 309 310
## 24.432701 36.703662 95.376066 26.962950 54.213304 72.750791 28.913744 93.549062 198.073464 142.929470 23.536288 88.417332 123.000000 165.648785 245.409837
## 311 312 313 314 315 316 317 318 320 321 322 323 324 325 326
## 260.424402 65.913947 238.512832 97.708942 287.391479 96.354546 177.000000 269.000000 51.045445 611.554296 82.794072 127.488362 35.238247 27.532422 83.768602
## 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341
## 138.527328 46.137382 116.000000 90.244691 46.942547 168.422770 233.322797 111.368361 417.000000 65.448155 18.424179 117.506577 159.000000 107.350651 100.861124
## 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356
## 148.463363 183.000000 246.000000 138.413997 89.422297 124.058565 47.306978 486.000000 92.240091 37.546970 51.041218 37.555518 35.021224 72.000000 53.493774
## 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371
## 41.443659 37.978901 76.025525 77.709883 42.858269 54.059825 39.003285 57.305301 65.879959 81.050764 70.898198 23.953827 77.249387 44.912574 41.625185
## 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386
## 52.106916 45.929901 60.508421 162.000000 38.731024 107.178853 81.846068 29.377330 105.824020 141.290961 39.941244 45.185014 80.222262 28.381931 60.631934
## 387 388 389 390 391 392 393 394 395 396 397 399 400 402 403
## 97.161879 45.448051 51.582852 30.352939 46.538911 177.000000 147.000000 58.000000 16.355081 46.377943 61.000000 16.947813 94.561591 38.898442 63.806498
## 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418
## 20.841090 23.012098 80.434096 126.987519 103.000000 45.375125 111.000000 142.000000 214.000000 99.000000 79.000000 376.000000 356.000000 403.000000 177.900880
## 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433
## 186.000000 240.313980 261.513423 311.031947 101.791007 338.000000 488.000000 230.420460 285.155917 223.183911 52.694383 62.213575 227.794677 208.201473 39.502458
## 434 435 436 437 438 439 440 441 443 444 446 447 448 449 450
## 311.675879 224.845111 184.753595 316.455568 177.674169 22.325859 96.693328 22.955767 87.000000 94.463654 115.546154 164.427569 282.522023 165.000000 274.814695
## 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465
## 144.397612 332.861550 207.074372 122.054739 313.000000 100.582497 153.002846 105.613631 124.930604 73.072703 248.171705 163.794030 105.015514 92.019414 115.969636
## 466 467 468 469 470 471 472 473 474 475 477 478 479 480 481
## 129.184980 86.637372 88.332850 106.206374 217.181458 80.428661 222.566011 86.226467 169.312499 255.921865 291.000000 20.011343 163.838002 42.000000 58.433644
## 482 483 484 485 486 488 489 490 491 492 493 494 495 496 497
## 33.000000 74.000000 16.011179 45.103741 29.825705 44.249204 176.825477 108.484942 129.522051 93.640108 191.207391 57.863537 132.438680 41.013224 61.771259
## 498 499 500 501 502 503 504 505 506 507 508 509 511 512 513
## 98.422107 66.084027 53.000000 64.961123 118.000000 67.475542 118.093572 85.258235 100.249819 145.656332 135.529494 82.778769 46.784720 150.354611 59.391070
## 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528
## 32.995352 83.871695 153.399417 169.030471 36.479729 93.000000 50.473905 55.423837 110.310671 73.797354 61.576185 110.280912 258.840651 28.445030 83.065528
## 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543
## 197.000000 145.105615 91.364087 35.146512 134.051099 64.743073 60.000000 16.384662 36.992880 48.806222 110.781932 37.000000 21.682092 37.053817 149.000000
## 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558
## 66.884810 119.333092 47.346808 93.036847 122.406777 73.616137 66.033020 97.201506 66.424207 77.000000 75.230211 45.197046 223.107309 97.000756 155.201537
## 559 560 561 562 563 564 565 566 567 568 569 570 571 572 574
## 97.905652 107.687258 116.569359 50.750481 111.942100 13.147827 153.000000 60.000000 158.186466 115.588852 97.373725 78.979673 55.596080 26.663391 139.550294
## 575 576 577 578 579 581 582 583 584 585 586 587 588 589 590
## 26.385679 237.731800 115.319173 59.711885 43.721161 35.708921 199.449535 149.822650 137.153646 33.533333 33.243109 212.153057 22.777126 50.957285 71.506114
## 592 593 594 595 596 597 598 601 603 604 605 606 608 609 610
## 65.034446 31.841298 60.610940 70.294994 35.763399 81.313346 52.455950 121.470894 53.475170 41.005329 15.680013 36.856808 74.088215 78.000000 116.741334
## 611 612 613 614 616 617 618 619 620 621 622 623 624 625 626
## 61.000000 141.000000 79.001162 115.832448 24.842153 79.073043 23.432935 9.824396 86.853251 11.497239 59.878465 121.147249 95.542126 39.000000 70.309130
## 627 628 629 630 631 632 633 634 635 638 641 642 643 644 645
## 50.596982 70.717697 88.700391 157.790627 359.000000 90.431052 45.376245 74.000000 59.621422 100.864142 68.878478 123.963095 201.055098 42.854084 77.000000
## 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660
## 82.000000 93.012070 55.550665 129.276161 73.852350 81.944684 26.288655 26.066565 46.093374 62.426679 38.290379 86.796375 60.645281 74.629583 15.000000
## 661 662 663 664 665 666 667 668 669 670 671 672 673 675 676
## 78.151925 71.268011 57.289255 30.806143 46.573552 40.528468 54.794393 68.456546 50.013498 118.886093 35.000000 43.579539 45.457806 86.164400 172.178620
## 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692
## 201.463361 86.255828 114.478316 34.408045 161.372654 265.153880 99.704631 67.404951 71.478280 31.000000 63.385607 19.490899 72.828611 95.918406 43.725270
## 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707
## 56.518624 157.898239 189.293855 104.595043 157.845446 72.906237 93.800562 98.068268 49.843993 165.895172 153.929108 15.495911 31.358866 194.181835 69.070250
## 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722
## 50.450276 81.093398 50.224879 84.683409 49.114997 101.245671 39.780666 86.515979 63.864439 113.228513 77.366741 50.900402 111.595088 88.000000 92.037129
## 723 724 725 726 727 728 730 731 732 733 734 735 736 737 738
## 86.008401 91.444218 125.521133 159.000000 103.412533 160.236642 36.357193 65.268387 51.920101 84.988311 293.246751 103.010844 127.824128 59.668524 144.180587
## 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753
## 82.845051 86.247724 227.955877 68.920197 23.735931 106.735759 156.000000 50.882368 79.129374 138.390070 88.226918 62.000000 13.468232 41.429813 57.677359
## 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768
## 293.420464 58.135971 207.712127 36.327832 243.150398 84.593226 115.362220 43.026927 179.468005 361.794596 74.876031 60.951114 284.960333 572.000000 300.212702
## 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783
## 274.000000 215.027223 158.000000 258.836879 267.000000 176.000000 275.475093 183.262254 289.000000 295.966452 82.921412 248.736808 91.000000 160.000000 301.547408
## 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798
## 71.535047 98.444482 94.348219 94.000000 99.390977 147.000000 14.718077 109.446213 75.412900 60.171027 74.521827 74.221809 26.000000 72.000000 50.000000
## 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813
## 68.021313 66.349656 47.240476 86.425050 88.888230 44.427439 45.817656 66.173523 85.525918 59.049518 88.755700 58.851296 85.994470 49.877365 175.753252
## 814 815 816 817 818 819 820 821 822 823 824 825 826 827 829
## 242.630212 112.641535 48.855622 198.998655 13.366547 155.232802 27.201926 140.870639 70.743562 56.528537 258.000000 78.104709 28.526137 110.717928 125.000000
## 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844
## 20.858046 78.972414 171.000000 43.172863 75.768388 260.187617 109.000000 91.179927 162.380295 46.176504 190.570845 102.774968 15.380248 72.350625 128.555261
## 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859
## 62.198743 37.423681 63.240469 122.000000 159.061214 86.766637 108.112853 55.708567 53.470835 65.723509 94.313516 81.749781 86.035168 77.664083 50.622291
## 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874
## 58.182250 285.112779 256.680411 74.452289 204.345140 100.862303 37.064650 173.211651 202.443986 328.653858 91.201617 101.682996 97.360755 64.042412 64.000000
## 875 876 877 879 880 881 882 883 884 885 886 887 888 889 890
## 77.000000 230.000000 77.000000 72.947431 92.000000 27.346620 143.177697 82.325315 218.946055 227.000000 85.755015 29.217814 66.330845 82.689158 42.000000
## 891 892 895 896 897 898 899 901 902 904 905 906 907 908 909
## 82.753854 71.799979 96.445544 218.550738 152.851753 19.547919 110.651850 308.000000 43.275352 87.000000 59.182312 58.954963 50.000000 67.000000 58.000000
## 910 911 912 913 914 915 916 918 919 920 921 922 923 924 925
## 88.097677 62.287808 21.905526 25.239919 60.527847 23.000000 43.280755 100.000000 12.996460 123.000000 130.579798 21.000000 40.877575 72.000000 23.442042
## 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940
## 142.000000 134.768705 91.087024 101.293070 203.050924 171.145622 168.658364 328.015694 270.068524 324.528912 194.836506 271.372681 341.000000 305.000000 344.066468
## 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955
## 245.193763 24.454865 80.001331 131.855428 31.159772 143.748413 70.783261 39.399805 101.387942 103.919772 34.138684 156.000000 240.000000 143.062266 146.000000
## 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970
## 103.679513 54.312588 27.772109 116.000000 44.674397 423.757051 71.720694 152.204748 122.000000 116.461030 79.000000 178.543817 49.000000 155.937563 167.624024
## 971 973 974 975 976 977 978 979 980 981 982 983 984 985 986
## 351.000000 76.804749 136.000000 185.000000 70.972130 182.741522 108.244183 47.265736 86.000000 129.939790 150.000000 186.000000 139.000000 225.000000 130.000000
## 987 988 990 991 992 993 994 995 996 997 998 999 1000 1001 1002
## 72.000000 111.162389 71.000000 99.120114 46.537395 255.733931 286.965909 34.087469 88.009418 128.479675 261.157908 247.714431 183.000000 158.393550 70.353923
## 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1014 1015 1016 1017 1018
## 334.000000 121.265619 118.878707 176.756353 147.033997 271.069630 210.733946 401.817812 77.673852 118.934536 262.586419 87.596575 241.821171 91.179945 149.509948
## 1019 1020 1021 1022 1023 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034
## 110.807957 63.364037 61.464891 55.473425 17.248604 139.749763 115.000000 51.496588 60.776072 24.719997 63.740545 42.582839 168.000000 18.452866 86.641983
## 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049
## 20.619155 65.422964 66.711500 90.150405 96.168502 58.019582 79.448644 36.049210 91.241657 33.634181 21.322700 54.157215 26.542752 35.608983 11.285010
## 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1064 1065 1067
## 28.999981 17.587124 33.662050 24.656772 24.185646 25.152650 27.677537 53.412719 21.824296 29.642109 13.406364 33.737010 16.528016 24.160765 16.952839
## 1068 1070 1072 1073 1074 1076 1079 1080 1081 1082
## 23.013005 14.084435 23.607497 28.365605 50.103711 32.487972 77.224977 45.825405 51.970844 86.780737
## [ reached 'max' / getOption("max.print") -- omitted 160420 entries ]What are the cells that have signal of contamination, but do not have neighbors of the secondary cell type around?
xe@meta.data <- xe@meta.data %>%
mutate(purified_with_no_secondary_type_sp_neighbors =
(rownames(xe@meta.data) %in% cells_that_uneffectively_send_transccripts) &
(rownames(xe@meta.data) %in% rownames(res_split$cell_meta)[res_split$cell_meta$purification_status == "purified"])
)
UMAPPlot(xe, group.by = "purified_with_no_secondary_type_sp_neighbors") + theme_void() + theme(aspect.ratio = 1)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
nb_weight_features <- c("neighborhood_weights_second_type", "neighborhood_weights_first_type")
nb_count_features <- c("second_type_neighbors_no_reject_N", "first_type_neighbors_N")
FeaturePlot(xe, features = nb_weight_features) & theme_void() & theme(aspect.ratio = 1)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
FeatureScatter(xe, feature1 = nb_weight_features[1], feature2 = nb_weight_features[2], group.by = "purified_with_no_secondary_type_sp_neighbors")## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
FeatureScatter(xe, feature1 = nb_weight_features[1], feature2 = nb_weight_features[2], group.by = "first_type", cols = pal)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
VlnPlot(xe, features = nb_count_features, group.by = "purified_with_no_secondary_type_sp_neighbors", pt.size = 0)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
df <- data.frame(
n_counts_pur = xe_purified_balanced_score$nCount_Xenium,
n_counts_reassigned = xe_post_redistribution$nCount_Xenium
)
plot(df$n_counts_pur, df$n_counts_reassigned)
abline(0,1)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.006 68.136 122.230 153.620 210.784 1240.000## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 5.017 79.907 140.134 170.442 231.000 1381.225xe_post_redistribution <- xe_post_redistribution %>%
SCTransform(assay = "Xenium", verbose = FALSE) %>%
RunPCA(verbose = FALSE) %>%
RunUMAP(dims = 1:20, verbose = FALSE)p_pr <- UMAPPlot(xe_post_redistribution, group.by = c("first_type"), cols = pal, label = T, repel = T) + ggtitle("Fully purified + counts reassignment") & theme(aspect.ratio = 1)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`p_pr_st <- UMAPPlot(xe_post_redistribution, group.by = c("second_type"), cols = pal) + ggtitle("Second type") & theme(aspect.ratio = 1)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`p_pur <- UMAPPlot(xe_purified, group.by = "first_type", cols = pal, label = T, repel = T) + ggtitle("Fully purified") & theme(aspect.ratio = 1) & NoLegend()## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
xe_post_redistribution$nCount_sent_uneffectively <- xe_post_redistribution$nCount_sent - xe_post_redistribution$nCount_sent_effectively
FeaturePlot(xe_post_redistribution, features = c("nCount_sent", "nCount_sent_effectively", "nCount_received")) & theme(aspect.ratio = 1)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
FeaturePlot(xe_post_redistribution, features = c("nCount_sent", "nCount_received", "nCount_sent_uneffectively", "nCount_sent_effectively"), raster = F) &
scale_color_gradientn(
colors = c("gray", "blue"),
#colours = viridis::viridis(100),
limits = c(NA, 150)
) &
theme(aspect.ratio = 1)## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
## Scale for colour is already present.
## Adding another scale for colour, which will replace the existing scale.
VlnPlot(xe_post_redistribution, features = c("nCount_sent", "nCount_received", "nCount_sent_uneffectively", "nCount_sent_effectively"), ncol = 2, group.by = "first_type", cols = pal, pt.size = 0, same.y.lims = T)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
VlnPlot(xe_post_redistribution, features = c("nCount_sent", "nCount_received", "nCount_sent_uneffectively", "nCount_sent_effectively"), ncol = 2, group.by = "second_type", cols = pal, pt.size = 0, same.y.lims = T)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
total_transcripts_raw <- rowSums(GetAssayData(xe, layer = "counts", assay = "Xenium"))
total_transcripts_raw_common_genes <- total_transcripts_raw[pur_genes]
total_transcripts_purified <- rowSums(GetAssayData(xe_purified_balanced_score, layer = "counts", assay = "Xenium"))
total_transcripts_purified_and_reassigned <- rowSums(GetAssayData(xe_post_redistribution, layer = "counts", assay = "Xenium"))
df_total_transcripts <- data.frame(
total_transcripts_raw_common_genes = total_transcripts_raw_common_genes,
total_transcripts_purified = total_transcripts_purified,
total_transcripts_purified_and_reassigned = total_transcripts_purified_and_reassigned
)
df_total_transcripts %>%
ggplot(aes(x = total_transcripts_raw_common_genes, y = total_transcripts_purified_and_reassigned)) +
geom_point() + geom_abline() +
scale_x_log10() + scale_y_log10() + theme_classic() + theme(aspect.ratio = 1)
df_plot <- data.frame(
category = factor(
c("Raw common genes", "Purified", "Purified + Reassigned"),
levels = c("Raw common genes", "Purified", "Purified + Reassigned") # enforce order
),
value = c(
sum(df_total_transcripts$total_transcripts_raw_common_genes),
sum(df_total_transcripts$total_transcripts_purified),
sum(df_total_transcripts$total_transcripts_purified_and_reassigned)
)
)
ggplot(df_plot, aes(x = category, y = value, fill = category)) +
geom_bar(stat = "identity", width = 0.6) +
labs(x = NULL, y = "Total transcripts") +
theme_minimal(base_size = 14) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 0, hjust = .5))
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## [1] 21002555residual_counts <- raw_counts[pur_genes, pur_cells] - res_split$purified_counts[pur_genes, pur_cells]
xe_residuals <- CreateSeuratObject(
counts = residual_counts,
meta.data = res_split$cell_meta,
assay = "Xenium"
)
xe_residuals <- subset(xe_residuals, nCount_Xenium > 1)
xe_residuals <- xe_residuals %>%
SCTransform(assay = "Xenium", verbose = FALSE) %>%
RunPCA(verbose = FALSE) %>%
RunUMAP(dims = 1:20, verbose = FALSE)Yes, the residuals group by the secondary (ie, residual) cell type
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`
VlnPlot(xe_residuals, features = "nCount_Xenium", group.by = "second_type", cols = pal, pt.size = 0)## Rasterizing points since number of points exceeds 100,000.
## To disable this behavior set `raster=FALSE`